A review of automatic liver tumor segmentation based on computed tomography_Journal of Biomedical Engineering

Authors：

YUE Meiyan ¹ , WEI Qianyue ¹ , DENG Wei ² , WANG Tianfu ¹ ,  DENG Yun ¹ , HUANG Bingsheng ¹

1. School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong 518060, P.R.China;
2. Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou 511400, P.R.China;

Corresponding?author：

Keywords：

liver cancer; computed tomography; machine learning; automatic segmentation

DOI：

10.7507/1001-5515.201708009

Video：

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Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.

Citation： YUE Meiyan, WEI Qianyue, DENG Wei, WANG Tianfu, DENG Yun, HUANG Bingsheng. A review of automatic liver tumor segmentation based on computed tomography. Journal of Biomedical Engineering, 2018, 35(3): 481-487. doi: 10.7507/1001-5515.201708009 Copy

1.	黃乾榮, 張玲. 原發性肝癌治療研究新進展. 實用醫學雜志, 2016, 32(14): 2275-2278.
2.	青格樂圖, 金剛.肝癌手術治療的研究進展. 中國民族民間醫藥, 2015, 24(4): 41-42.
3.	胡貝貝. 層析芯片陽性信號的信息提取和定量分析研究. 上海: 上海交通大學, 2010.
4.	黃芳. 圖像分割方法研究. 赤峰學院學報: 自然科學版, 2016, 32(21): 20-21.
5.	Sethi G, Saini B S, Singh D. Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method. Computers & Electrical Engineering, 2016, 53: 244-262.
6.	Patil S, Udupi V R, Patole D. A robust system for segmentation of primary liver tumor in CT images. Int J Comput Appl, 2013, 75(13): 6-10.
7.	Baazaoui A, Barhoumi W, Ahmed A, et al. Semi-Automated segmentation of single and multiple tumors in liver CT images using Entropy-Based fuzzy region growing. IRBM, 2017, 38(2): 98-108.
8.	Abd-Elaziz O F, Sayed M S, Abdullah M I. Liver tumors segmentation from abdominal CT images using region growing and morphological processing// International Conference on Engineering and Technology, Cairo, 2015:1-6.
9.	Yan Jiayong, Schwartz L H, Zhao Binsheng. Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys, 2015, 42(11): 6283-6293.
10.	Xing Z, Jie T, Xiang D, et al. Interactive liver tumor segmentation from ct scans using support vector classification with watershed// International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, 2011:6005-6008.
11.	Li Bingnan, Chui C K, Chang S, et al. A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl, 2012, 39(10): 9661-9668.
12.	Amarajothi T N, Manikandan S, Muthukkutti K. Liver tumor segmentation using single level set method with shape and intensity prior. International Journal of Applied Engineering Research, 2015, 10(20): 15717-15721.
13.	Cui Y, Wang X, Yu H. Level set liver tumor segmentation based on parameterized morphological gradient modification. Application Research of Computers, 2015, 7: 068.
14.	Cai X D, Wu D, Su Z E, et al. Entanglement-based machine learning on a quantum computer. Phys Rev Lett, 2015, 114(11): 110504.
15.	Zhou Jiayin, Huang Weimin, Xiong Wei, et al. Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework// 2013 35th Annual International Conference Of The IEEE Engineering In Medicine & Biology Society (EMBC), Osaka, 2013: 3347-3350.
16.	Li W, Jia F, Hu Q. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Journal of Computer & Communications, 2015, 03(11): 146-151.
17.	Hoogi A, Subramaniam A, Veerapaneni R, et al. Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. IEEE Trans Med Imaging, 2017, 36(3): 781-791.
18.	Christ P F, Elshaer M E A, Ettlinger F, et al. Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3D conditional random fields // International Conference on Medical Image Computing and Computer-Assisted Intervention, Istanbul, 2016:415-423.
19.	Conze P H, Noblet V, Heitz F, et al. Semi-automatic liver tumor segmentation in dynamic contrast-enhanced CT scans using random forests and supervoxels// International Workshop on Machine Learning in Medical Imaging, Munich, 2015:212-219.
20.	Conze P H, Noblet V, Rousseau F, et al. Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced ct scans// 2016 IEEE 13th International Symposium On Biomedical Imaging (ISBI), Prague, 2016: 416-419.
21.	宋坤. 聚類算法綜述. 河南科技, 2015(22): 254.
22.	Sajith A G, Hariharan S. Spatial fuzzy C-means Clustering based Segmentation on CT Images// 2015 2nd International Conference On Electronics And Communication Systems (ICECS), 2015: 414-417.
23.	Obayya M, Ei R S. Automated segmentation of suspicious regions in liver CT using FCM. Int J Comput Appl, 2015, 118(6): 1-4.
24.	Ben-Cohen A, Klang E, Diamant I, et al. Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations. Journal of Medical Imaging, 2015, 2(3): 034502.
25.	Devi P, Dabas P. Liver tumor detection using artificial neural networks for medical images. International Journal of Innovative Research in Science & Technology, 2015, 2(3): 34-38.
26.	Kadoury S, Vorontsov E, Tang An. Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol, 2015, 60(16): 6459-6478.

1. 黃乾榮, 張玲. 原發性肝癌治療研究新進展. 實用醫學雜志, 2016, 32(14): 2275-2278.
2. 青格樂圖, 金剛.肝癌手術治療的研究進展. 中國民族民間醫藥, 2015, 24(4): 41-42.
3. 胡貝貝. 層析芯片陽性信號的信息提取和定量分析研究. 上海: 上海交通大學, 2010.
4. 黃芳. 圖像分割方法研究. 赤峰學院學報: 自然科學版, 2016, 32(21): 20-21.
5. Sethi G, Saini B S, Singh D. Segmentation of cancerous regions in liver using an edge-based and phase congruent region enhancement method. Computers & Electrical Engineering, 2016, 53: 244-262.
6. Patil S, Udupi V R, Patole D. A robust system for segmentation of primary liver tumor in CT images. Int J Comput Appl, 2013, 75(13): 6-10.
7. Baazaoui A, Barhoumi W, Ahmed A, et al. Semi-Automated segmentation of single and multiple tumors in liver CT images using Entropy-Based fuzzy region growing. IRBM, 2017, 38(2): 98-108.
8. Abd-Elaziz O F, Sayed M S, Abdullah M I. Liver tumors segmentation from abdominal CT images using region growing and morphological processing// International Conference on Engineering and Technology, Cairo, 2015:1-6.
9. Yan Jiayong, Schwartz L H, Zhao Binsheng. Semiautomatic segmentation of liver metastases on volumetric CT images. Med Phys, 2015, 42(11): 6283-6293.
10. Xing Z, Jie T, Xiang D, et al. Interactive liver tumor segmentation from ct scans using support vector classification with watershed// International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, 2011:6005-6008.
11. Li Bingnan, Chui C K, Chang S, et al. A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl, 2012, 39(10): 9661-9668.
12. Amarajothi T N, Manikandan S, Muthukkutti K. Liver tumor segmentation using single level set method with shape and intensity prior. International Journal of Applied Engineering Research, 2015, 10(20): 15717-15721.
13. Cui Y, Wang X, Yu H. Level set liver tumor segmentation based on parameterized morphological gradient modification. Application Research of Computers, 2015, 7: 068.
14. Cai X D, Wu D, Su Z E, et al. Entanglement-based machine learning on a quantum computer. Phys Rev Lett, 2015, 114(11): 110504.
15. Zhou Jiayin, Huang Weimin, Xiong Wei, et al. Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework// 2013 35th Annual International Conference Of The IEEE Engineering In Medicine & Biology Society (EMBC), Osaka, 2013: 3347-3350.
16. Li W, Jia F, Hu Q. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. Journal of Computer & Communications, 2015, 03(11): 146-151.
17. Hoogi A, Subramaniam A, Veerapaneni R, et al. Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. IEEE Trans Med Imaging, 2017, 36(3): 781-791.
18. Christ P F, Elshaer M E A, Ettlinger F, et al. Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3D conditional random fields // International Conference on Medical Image Computing and Computer-Assisted Intervention, Istanbul, 2016:415-423.
19. Conze P H, Noblet V, Heitz F, et al. Semi-automatic liver tumor segmentation in dynamic contrast-enhanced CT scans using random forests and supervoxels// International Workshop on Machine Learning in Medical Imaging, Munich, 2015:212-219.
20. Conze P H, Noblet V, Rousseau F, et al. Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced ct scans// 2016 IEEE 13th International Symposium On Biomedical Imaging (ISBI), Prague, 2016: 416-419.
21. 宋坤. 聚類算法綜述. 河南科技, 2015(22): 254.
22. Sajith A G, Hariharan S. Spatial fuzzy C-means Clustering based Segmentation on CT Images// 2015 2nd International Conference On Electronics And Communication Systems (ICECS), 2015: 414-417.
23. Obayya M, Ei R S. Automated segmentation of suspicious regions in liver CT using FCM. Int J Comput Appl, 2015, 118(6): 1-4.
24. Ben-Cohen A, Klang E, Diamant I, et al. Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations. Journal of Medical Imaging, 2015, 2(3): 034502.
25. Devi P, Dabas P. Liver tumor detection using artificial neural networks for medical images. International Journal of Innovative Research in Science & Technology, 2015, 2(3): 34-38.
26. Kadoury S, Vorontsov E, Tang An. Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol, 2015, 60(16): 6459-6478.

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